SbD4Skin by EosCloud: Integrating multi-view molecular representation for predicting skin sensitization, irritation, and acute dermal toxicity.

ABSTRACT: Assessing chemical toxicity is essential for understanding potential risks to human health. However, ethical, financial, and scientific challenges have driven the demand for non-animal testing methods. This study introduces a computational framework that leverages diverse molecular representations, including MACCS keys, Morgan fingerprints, and Mordred descriptors, to predict skin sensitization, irritation/corrosion, and acute dermal toxicity. Different molecular representations for skin toxicity-related endpoints were first evaluated using three machine learning algorithms (Random Forest, Support Vector Machine, and k-Nearest Neighbors), then combined into a unified input space for training a fully connected neural network (FCNN). Comparative analyses indicate that this multi-view FCNN mo

SUBMITTER: Koutroumpa NM 

PROVIDER: S-EPMC12358665 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature